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    "# 商务智能与数据挖掘课程：工程化实践路线\n",
    "\n",
    "## 为什么选择Python进行数据处理与挖掘？\n",
    "\n",
    "- **易学易用**：语法简洁清晰，入门门槛低\n",
    "- **丰富的生态系统**：拥有大量数据科学和机器学习相关库\n",
    "- **跨平台兼容性**：可在各种操作系统上运行\n",
    "- **活跃的社区支持**：问题解决和资源丰富\n",
    "- **工业界广泛应用**：从创业公司到大型科技企业都在使用\n",
    "\n",
    "## 我们的技术栈\n",
    "\n",
    "### 1. 数据获取与爬虫\n",
    "\n",
    "- Python爬虫框架：Requests, BeautifulSoup, Scrapy\n",
    "- API接口调用：REST API, GraphQL\n",
    "- 数据格式处理：JSON, XML, CSV\n",
    "\n",
    "### 2. 数据处理与分析\n",
    "\n",
    "- 数据处理：Pandas, NumPy\n",
    "- 数据可视化：Matplotlib, Seaborn, Plotly\n",
    "- 数据库操作：SQLAlchemy, PyMongo\n",
    "\n",
    "### 3. 机器学习工程化\n",
    "\n",
    "- 传统机器学习：Scikit-learn\n",
    "- 自动化机器学习：AutoML工具\n",
    "- 模型部署与服务：Flask/FastAPI, Docker\n",
    "\n",
    "### 4. 大模型API应用\n",
    "\n",
    "- OpenAI API (GPT系列)\n",
    "- Anthropic API (Claude系列)\n",
    "- 国内大模型API (SeepSeek等)\n",
    "- LangChain/LlamaIndex框架\n",
    "\n",
    "### 5. 实战项目技术\n",
    "\n",
    "- 全流程数据分析\n",
    "- Web应用开发与部署\n",
    "- 商业智能可视化平台\n",
    "\n",
    "从数据获取开始，我们将构建完整的数据处理分析流程，到最终的商业应用落地，全面掌握数据挖掘工程化所需技能。"
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